Cluster analysis. (Lecture 6-8)

1. Data Mining: Lecture 6-8: CLUSTER ANALYSIS —

Ph.D. Shatovskaya T.
Department of Computer Science
January 23, 2017
Data Mining: Concepts and Techniques
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2. Chapter 8. Cluster Analysis

What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
January 23, 2017
Data Mining: Concepts and Techniques
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3. What is Cluster Analysis?

General Applications of Clustering
Pattern Recognition
Spatial Data Analysis
create thematic maps in GIS by clustering feature
spaces
detect spatial clusters and explain them in spatial data
mining
Image Processing
Economic Science (especially market research)
WWW
Document classification
Cluster Weblog data to discover groups of similar
access patterns
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4. General Applications of Clustering

Examples of Clustering Applications
Marketing: Help marketers discover distinct groups in
their customer bases, and then use this knowledge to
develop targeted marketing programs
Land use: Identification of areas of similar land use in an
earth observation database
Insurance: Identifying groups of motor insurance policy
holders with a high average claim cost
City-planning: Identifying groups of houses according to
their house type, value, and geographical location
Earth-quake studies: Observed earth quake epicenters
should be clustered along continent faults
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5. Examples of Clustering Applications

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6.

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7.

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8.

What Is Good Clustering?
A good clustering method will produce high quality
clusters with
high intra-class similarity
low inter-class similarity
The quality of a clustering result depends on both the
similarity measure used by the method and its
implementation.
The quality of a clustering method is also measured by
its ability to discover some or all of the hidden patterns.
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9. What Is Good Clustering?

Requirements of Clustering in Data
Mining
Scalability
Ability to deal with different types of attributes
Discovery of clusters with arbitrary shape
Minimal requirements for domain knowledge to
determine input parameters
Able to deal with noise and outliers
Insensitive to order of input records
High dimensionality
Incorporation of user-specified constraints
Interpretability and usability
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10. Requirements of Clustering in Data Mining

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
January 23, 2017
Data Mining: Concepts and Techniques
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11. Chapter 8. Cluster Analysis

Data Structures
Data matrix
(two modes)
Dissimilarity matrix
(one mode)
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x11
...
x
i1
...
x
n1
...
x1f
...
...
...
...
xif
...
...
...
...
... xnf
...
...
0
d(2,1)
0
d(3,1) d ( 3,2) 0
:
:
:
d ( n,1) d ( n,2) ...
Data Mining: Concepts and Techniques
x1p
...
xip
...
xnp
... 0
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12. Data Structures

Measure the Quality of Clustering
Dissimilarity/Similarity metric: Similarity is expressed in
terms of a distance function, which is typically metric:
d(i, j)
There is a separate “quality” function that measures the
“goodness” of a cluster.
The definitions of distance functions are usually very
different for interval-scaled, boolean, categorical, ordinal
and ratio variables.
Weights should be associated with different variables
based on applications and data semantics.
It is hard to define “similar enough” or “good enough”
the answer is typically highly subjective.
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13. Measure the Quality of Clustering

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14.

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15.

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16.

Type of data in clustering analysis
Interval-scaled variables:
Binary variables:
Nominal, ordinal, and ratio variables:
Variables of mixed types:
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17. Type of data in clustering analysis

Interval-valued variables
Standardize data
Calculate the mean absolute deviation:
sf 1
n (| x1 f m f | | x2 f m f | ... | xnf m f |)
where
m f 1n (x1 f x2 f
...
xnf )
.
Calculate the standardized measurement (z-score)
xif m f
zif
sf
Using mean absolute deviation is more robust than using
standard deviation
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18. Interval-valued variables

Binary Variables
A contingency table for binary data
Object j
1
Object i
0
sum
1
a
b
0
c
d
sum a c b d
a b
c d
p
Simple matching coefficient (invariant, if the binary
b c
variable is symmetric):
d (i, j)
a b c d
Jaccard coefficient (noninvariant if the binary variable is
asymmetric):
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d (i, j)
b c
a b c
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19. Binary Variables

Rassel and Rao coefficient: J(i,j)= a/ a+b+c+d
Bravais coefficient: C(i,j)= ad-bc/ (a b)(a c)(d b)(d c)
Association coefficient Yule: Q(i,j)= ad-bc/ ad+bc
Hemming distance: H(i,j)= a+d
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20.

Dissimilarity between Binary
Variables
Example
Name
Jack
Mary
Jim
Gender
M
F
M
Fever
Y
Y
Y
Cough
N
N
P
Test-1
P
P
N
Test-2
N
N
N
Test-3
N
P
N
Test-4
N
N
N
gender is a symmetric attribute
the remaining attributes are asymmetric binary
let the values Y and P be set to 1, and the value N be set to 0
0 1
0.33
2 0 1
1 1
d ( jack , jim )
0.67
1 1 1
1 2
d ( jim , mary )
0.75
1 1 2
d ( jack , mary )
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21. Dissimilarity between Binary Variables

Nominal Variables
A generalization of the binary variable in that it can take
more than 2 states, e.g., red, yellow, blue, green
Method 1: Simple matching
m: # of matches, p: total # of variables
m
d (i, j) p
p
Method 2: use a large number of binary variables
creating a new binary variable for each of the M
nominal states
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22. Nominal Variables

Ordinal Variables
An ordinal variable can be discrete or continuous
Order is important, e.g., rank
Can be treated like interval-scaled
replace xif by their rank
map the range of each variable onto [0, 1] by replacing
i-th object in the f-th variable by
zif
rif {1,...,M f }
rif 1
M f 1
compute the dissimilarity using methods for intervalscaled variables
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23. Ordinal Variables

Ratio-Scaled Variables
Ratio-scaled variable: a positive measurement on a
nonlinear scale, approximately at exponential scale,
such as AeBt or Ae-Bt
Methods:
treat them like interval-scaled variables—not a good
choice! (why?—the scale can be distorted)
apply logarithmic transformation
yif = log(xif)
treat them as continuous ordinal data treat their rank
as interval-scaled
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24. Ratio-Scaled Variables

Variables of Mixed Types
A database may contain all the six types of variables
symmetric binary, asymmetric binary, nominal,
ordinal, interval and ratio
One may use a weighted formula to combine their
effects
pf 1 ij( f ) dij( f )
d (i, j)
pf 1 ij( f )
f is binary or nominal:
dij(f) = 0 if xif = xjf , or dij(f) = 1 o.w.
f is interval-based: use the normalized distance
f is ordinal or ratio-scaled
compute ranks rif and
r 1
z
if
and treat zif as interval-scaled
M 1
if
f
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25. Variables of Mixed Types

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
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26. Chapter 8. Cluster Analysis

Major Clustering Approaches
Partitioning algorithms: Construct various partitions and
then evaluate them by some criterion
Hierarchy algorithms: Create a hierarchical decomposition
of the set of data (or objects) using some criterion
Density-based: based on connectivity and density functions
Grid-based: based on a multiple-level granularity structure
Model-based: A model is hypothesized for each of the
clusters and the idea is to find the best fit of that model to
each other
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Data Mining: Concepts and Techniques
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27. Major Clustering Approaches

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
January 23, 2017
Data Mining: Concepts and Techniques
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28. Chapter 8. Cluster Analysis

Partitioning Algorithms: Basic Concept
Partitioning method: Construct a partition of a database D
of n objects into a set of k clusters
Given a k, find a partition of k clusters that optimizes the
chosen partitioning criterion
Global optimal: exhaustively enumerate all partitions
Heuristic methods: k-means and k-medoids algorithms
k-means (MacQueen’67): Each cluster is represented
by the center of the cluster
k-medoids or PAM (Partition around medoids)
(Kaufman & Rousseeuw’87): Each cluster is
represented by one of the objects in the cluster
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29. Partitioning Algorithms: Basic Concept

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30.

The K-Means Clustering Method
Given k, the k-means algorithm is implemented in
four steps:
Partition objects into k nonempty subsets
Compute seed points as the centroids of the
clusters of the current partition (the centroid is the
center, i.e., mean point, of the cluster)
Assign each object to the cluster with the nearest
seed point
Go back to Step 2, stop when no more new
assignment
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31. The K-Means Clustering Method

Example
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cluster center
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each
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center
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Update
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32. The K-Means Clustering Method

Comments on the K-Means Method
Strength: Relatively efficient: O(tkn), where n is # objects, k is #
clusters, and t is # iterations. Normally, k, t << n.
Comparing: PAM: O(k(n-k)2 ), CLARA: O(ks2 + k(n-k))
Comment: Often terminates at a local optimum. The global
optimum may be found using techniques such as: deterministic
annealing and genetic algorithms
Weakness
Applicable only when mean is defined, then what about
categorical data?
Need to specify k, the number of clusters, in advance
Unable to handle noisy data and outliers
Not suitable to discover clusters with non-convex shapes
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33. Comments on the K-Means Method

Variations of the K-Means Method
A few variants of the k-means which differ in
Selection of the initial k means
Dissimilarity calculations
Strategies to calculate cluster means
Handling categorical data: k-modes (Huang’98)
Replacing means of clusters with modes
Using new dissimilarity measures to deal with categorical objects
Using a frequency-based method to update modes of clusters
A mixture of categorical and numerical data: k-prototype method
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34. Variations of the K-Means Method

What is the problem of k-Means Method?
The k-means algorithm is sensitive to outliers !
Since an object with an extremely large value may substantially
distort the distribution of the data.
K-Medoids: Instead of taking the mean value of the object in a
cluster as a reference point, medoids can be used, which is the most
centrally located object in a cluster.
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35. What is the problem of k-Means Method?

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36.

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37.

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38.

Typical k-medoids algorithm (PAM)
Total Cost = 20
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Arbitrary
choose k
object as
initial
medoids
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Until no
change
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swapping
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Swapping O
and Oramdom
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If quality is
improved.
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nonmedoid object,Oramdom
Total Cost = 26
Do loop
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39. Typical k-medoids algorithm (PAM)

What is the problem with PAM?
Pam is more robust than k-means in the presence of
noise and outliers because a medoid is less influenced by
outliers or other extreme values than a mean
Pam works efficiently for small data sets but does not
scale well for large data sets.
O(k(n-k)2 ) for each iteration
where n is # of data,k is # of clusters
Sampling based method,
CLARA(Clustering LARge Applications)
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40. What is the problem with PAM?

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41.

CLARA (Clustering Large Applications) (1990)
CLARA (Kaufmann and Rousseeuw in 1990)
Built in statistical analysis packages, such as S+
It draws multiple samples of the data set, applies PAM on
each sample, and gives the best clustering as the output
Strength: deals with larger data sets than PAM
Weakness:
Efficiency depends on the sample size
A good clustering based on samples will not
necessarily represent a good clustering of the whole
data set if the sample is biased
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42. CLARA (Clustering Large Applications) (1990)

CLARANS (“Randomized” CLARA) (1994)
CLARANS (A Clustering Algorithm based on Randomized
Search) (Ng and Han’94)
CLARANS draws sample of neighbors dynamically
The clustering process can be presented as searching a
graph where every node is a potential solution, that is, a
set of k medoids
If the local optimum is found, CLARANS starts with new
randomly selected node in search for a new local optimum
It is more efficient and scalable than both PAM and CLARA
Focusing techniques and spatial access structures may
further improve its performance (Ester et al.’95)
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43. CLARANS (“Randomized” CLARA) (1994)

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46.

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47.

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
January 23, 2017
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48. Chapter 8. Cluster Analysis

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49.

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50.

A Dendrogram Shows How the
Clusters are Merged Hierarchically
Decompose data objects into a several levels of nested
partitioning (tree of clusters), called a dendrogram.
A clustering of the data objects is obtained by cutting the
dendrogram at the desired level, then each connected
component forms a cluster.
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51.

A Dendrogram Algorithm for Binary
variables
1. To estimate similarity of objects on the basis of binary
attributes and measures of similarity of objects such as
Simple matching coefficient, Jaccard coefficient, Rassel and
Rao coefficient, Bravais coefficient, association coefficient
Yule, Hemming distance.
2.To make a incedence matrix for all objects, where it’s
elements is similarity coefficients.
3. Graphically represent a incedence matrix where on an axis
х – number of objects, on an axis Y –the measures of
similarity. Find in a matrix two most similar objects (with the
minimal distance) and put them on the schedule. Iteratively
continue construction of the schedule for all objects of the
analysis
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52. A Dendrogram Algorithm for Binary variables

Example for binary variables
We have 3 objects with 16 attributes . Define the
similarity of objects.
ecoli1
ecoli2
ecoli3
0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 1
0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0
1 1 0 1 1 0 0 1 0 0 0 0 0 0 1 1
1. Define the similarity on the base of Simple matching
coefficient
ecoli1
1
0
1
0
ecoli3
ecoli1
1 4
2
J13=12/15=0.8
ecoli2 1 4 1 J12=13/16=0.81
0
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2 9
0
1 8
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53. Example for binary variables

ecoli2 1
ecoli3
0
1
5
0
2
0
9
J23=14/16=0.875
2. Incedence matrix
ecoli1 ecoli2 ecoli3
ecoli1 0
0.81 0.8
ecoli2
0
0.875
0.81
0.8
ecoli3
2
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Data Mining: Concepts and Techniques
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3
number
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54. Example for binary variables

A Dendrogram Algorithm for
Numerical variables
1. To estimate similarity of objects on the basis of numerical
attributes and measures of similarity of objects such as
distances (slide 14).
2.To make a incedence matrix for all objects, where it’s
elements is distances.
3. Graphically represent a incedence matrix where on an axis
х – number of objects, on an axis Y –the measures of
similarity. Find in a matrix two most similar objects (with the
minimal distance) and put them on the schedule. Iteratively
continue construction of the schedule for all objects of the
analysis
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55. A Dendrogram Algorithm for Numerical variables

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56.

A Dendrogram Algorithm for
Numerical variables
Let us consider five points {x1,….,x5} with the attributes
X1=(0,2), x2=(0,0) x3=(1.5,0) x4=(5,0) x5=(5,2)
Using Euclidian measure
Cluster 2
Cluster 1
a) single-link distance
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Cluster 2
Cluster 1
b) complete-link distance
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57. A Dendrogram Algorithm for Numerical variables

D(x1,x2)=2 D(x1,x3)=2.5 D(x1,x4)=5.39 D(x1,x5)=5
D(x2,x3)=1.5 D(x2,x4)=5 D(x2,x5)=5.29
D(x3,x4)=3.5 D(x3,x5)=4.03
D(x4,x5)=2
5.4
3.5
2.2
2.5
2
2
1.5
1.5
x2 x3
x1
x4
x2 x3
x5
Dendrogram by single-link method
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x1
x4
x5
Dendrogram by complete-link
method
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58. A Dendrogram Algorithm for Numerical variables

Hierarchical Clustering
Use distance matrix as clustering criteria. This method
does not require the number of clusters k as an input,
but needs a termination condition
Step 0
a
Step 1
Step 2 Step 3 Step 4
agglomerative
(AGNES)
ab
b
abcde
c
cde
d
de
e
Step 4
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Step 3
Step 2 Step 1 Step 0
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divisive
(DIANA)
59

59. Hierarchical Clustering

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60.

AGNES (Agglomerative Nesting)
Introduced in Kaufmann and Rousseeuw (1990)
Implemented in statistical analysis packages, e.g., Splus
Use the Single-Link method and the dissimilarity matrix.
Merge nodes that have the least dissimilarity
Go on in a non-descending fashion
Eventually all nodes belong to the same cluster
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61. AGNES (Agglomerative Nesting)

DIANA (Divisive Analysis)
Introduced in Kaufmann and Rousseeuw (1990)
Implemented in statistical analysis packages, e.g., Splus
Inverse order of AGNES
Eventually each node forms a cluster on its own
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62. DIANA (Divisive Analysis)

More on Hierarchical Clustering Methods
Major weakness of agglomerative clustering methods
2
do not scale well: time complexity of at least O(n ),
where n is the number of total objects
can never undo what was done previously
Integration of hierarchical with distance-based clustering
BIRCH (1996): uses CF-tree and incrementally adjusts
the quality of sub-clusters
CURE (1998): selects well-scattered points from the
cluster and then shrinks them towards the center of the
cluster by a specified fraction
CHAMELEON (1999): hierarchical clustering using
dynamic modeling
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63. More on Hierarchical Clustering Methods

BIRCH (1996)
Birch: Balanced Iterative Reducing and Clustering using
Hierarchies, by Zhang, Ramakrishnan, Livny (SIGMOD’96)
Incrementally construct a CF (Clustering Feature) tree, a
hierarchical data structure for multiphase clustering
Phase 1: scan DB to build an initial in-memory CF tree (a
multi-level compression of the data that tries to preserve
the inherent clustering structure of the data)
Phase 2: use an arbitrary clustering algorithm to cluster
the leaf nodes of the CF-tree
Scales linearly: finds a good clustering with a single scan
Weakness: handles only numeric data, and sensitive to the
and improves the quality with a few additional scans
order of the data record.
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64. BIRCH (1996)

Clustering Feature Vector
Clustering Feature: CF = (N, LS, SS)
N: Number of data points
LS: Ni=1=Xi
SS: Ni=1=Xi2
CF = (5, (16,30),(54,190))
10
(3,4)
(2,6)
(4,5)
(4,7)
(3,8)
9
8
7
6
5
4
3
2
1
0
0
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2
3
4
5
6
7
8
9
10
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65.

CF-Tree in BIRCH
Clustering feature:
summary of the statistics for a given subcluster: the 0-th, 1st and
2nd moments of the subcluster from the statistical point of view.
registers crucial measurements for computing cluster and utilizes
storage efficiently
A CF tree is a height-balanced tree that stores the clustering features
for a hierarchical clustering
A nonleaf node in a tree has descendants or “children”
The nonleaf nodes store sums of the CFs of their children
A CF tree has two parameters
Branching factor: specify the maximum number of children.
threshold: max diameter of sub-clusters stored at the leaf nodes
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66. CF-Tree in BIRCH

CF Tree
Root
B=7
CF1
CF2 CF3
CF6
L=6
child1
child2 child3
child6
CF1
Non-leaf node
CF2 CF3
CF5
child1
child2 child3
child5
Leaf node
prev CF1 CF2
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CF6 next
Leaf node
prev CF1 CF2
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CF4 next
67

67. CF Tree

CURE (Clustering Using
REpresentatives )
CURE: proposed by Guha, Rastogi & Shim, 1998
Stops the creation of a cluster hierarchy if a level
consists of k clusters
Uses multiple representative points to evaluate the
distance between clusters, adjusts well to arbitrary
shaped clusters and avoids single-link effect
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68. CURE (Clustering Using REpresentatives )

Drawbacks of Distance-Based
Method
Drawbacks of square-error based clustering method
Consider only one point as representative of a cluster
Good only for convex shaped, similar size and density,
and if k can be reasonably estimated
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69. Drawbacks of Distance-Based Method

Cure: The Algorithm
Draw random sample s.
Partition sample to p partitions with size s/p
Partially cluster partitions into s/pq clusters
Eliminate outliers
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By random sampling
If a cluster grows too slow, eliminate it.
Cluster partial clusters.
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70. Cure: The Algorithm

Data Partitioning and Clustering
s = 50
p=2
s/p = 25
s/pq = 5
y
y
y
x
y
y
x
x
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x
71

71. Data Partitioning and Clustering

Cure: Shrinking Representative Points
y
y
x
x
Shrink the multiple representative points towards the
gravity center by a fraction of .
Multiple representatives capture the shape of the cluster
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72. Cure: Shrinking Representative Points

Clustering Categorical Data: ROCK
ROCK: Robust Clustering using linKs,
by S. Guha, R. Rastogi, K. Shim (ICDE’99).
Use links to measure similarity/proximity
Not distance based
2
2
O
(
n
nm
m
n
log n)
Computational complexity:
m a
Basic ideas:
Similarity function and neighbors: Sim( T , T ) T T
T T
Let T1 = {1,2,3}, T2={3,4,5}
1
Sim( T1, T 2)
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2
1
2
2
{3}
1
0.2
{1,2,3,4,5}
5
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73. Clustering Categorical Data: ROCK

Rock: Algorithm
Links: The number of common neighbors for the
two points.
{1,2,3}, {1,2,4}, {1,2,5}, {1,3,4}, {1,3,5}
{1,4,5}, {2,3,4}, {2,3,5}, {2,4,5}, {3,4,5}
3
{1,2,3}
{1,2,4}
Algorithm
Draw random sample
Cluster with links
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74. Rock: Algorithm

CHAMELEON (Hierarchical clustering
using dynamic modeling)
CHAMELEON: by G. Karypis, E.H. Han, and V. Kumar’99
Measures the similarity based on a dynamic model
Two clusters are merged only if the interconnectivity and
closeness (proximity) between two clusters are high relative to the
internal interconnectivity of the clusters and closeness of items
within the clusters
Cure ignores information about interconnectivity of the objects,
Rock ignores information about the closeness of two clusters
A two-phase algorithm
1. Use a graph partitioning algorithm: cluster objects into a large
number of relatively small sub-clusters
2. Use an agglomerative hierarchical clustering algorithm: find the
genuine clusters by repeatedly combining these sub-clusters
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75. CHAMELEON (Hierarchical clustering using dynamic modeling)

Overall Framework of CHAMELEON
Construct
Partition the Graph
Sparse Graph
Data Set
Merge Partition
Final Clusters
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76. Overall Framework of CHAMELEON

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
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77. Chapter 8. Cluster Analysis

Density-Based Clustering Methods
Clustering based on density (local cluster criterion),
such as density-connected points
Major features:
Discover clusters of arbitrary shape
Handle noise
One scan
Need density parameters as termination condition
Several interesting studies:
DBSCAN: Ester, et al. (KDD’96)
OPTICS: Ankerst, et al (SIGMOD’99).
DENCLUE: Hinneburg & D. Keim (KDD’98)
CLIQUE: Agrawal, et al. (SIGMOD’98)
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78. Density-Based Clustering Methods

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79.

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80.

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81.

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82.

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83.

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84.

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85.

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86.

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87.

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88

88.

Gradient: The steepness of a slope
Example
f Gaussian ( x , y ) e
f
D
Gaussian
f
( x ) i 1 e
N
d ( x , xi ) 2
2 2
( x, xi ) i 1 ( xi x) e
D
Gaussian
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d ( x , y )2
2 2
N
Data Mining: Concepts and Techniques
d ( x , xi ) 2
2 2
89

89. Gradient: The steepness of a slope

Density Attractor
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90. Density Attractor

Center-Defined and Arbitrary
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91. Center-Defined and Arbitrary

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92.

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93.

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94

94.

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95

95.

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96

96.

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97

97.

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
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98. Chapter 8. Cluster Analysis

Grid-Based Clustering Method
Using multi-resolution grid data structure
Several interesting methods
STING (a STatistical INformation Grid approach)
by Wang, Yang and Muntz (1997)
WaveCluster by Sheikholeslami, Chatterjee, and
Zhang (VLDB’98)
A multi-resolution clustering approach using
wavelet method
CLIQUE: Agrawal, et al. (SIGMOD’98)
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99. Grid-Based Clustering Method

STING: A Statistical Information
Grid Approach
Wang, Yang and Muntz (VLDB’97)
The spatial area area is divided into rectangular cells
There are several levels of cells corresponding to different
levels of resolution
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100. STING: A Statistical Information Grid Approach

(2)
Each cell at a high level is partitioned into a number of
smaller cells in the next lower level
Statistical info of each cell is calculated and stored
beforehand and is used to answer queries
Parameters of higher level cells can be easily calculated from
parameters of lower level cell
count, mean, s, min, max
type of distribution—normal, uniform, etc.
Use a top-down approach to answer spatial data queries
Start from a pre-selected layer—typically with a small
number of cells
For each cell in the current level compute the confidence
interval

101. STING: A Statistical Information Grid Approach (2)

STING: A Statistical Information
Grid Approach (3)
Remove the irrelevant cells from further consideration
When finish examining the current layer, proceed to
the next lower level
Repeat this process until the bottom layer is reached
Advantages:
Query-independent, easy to parallelize, incremental
update
O(K), where K is the number of grid cells at the
lowest level
Disadvantages:
All the cluster boundaries are either horizontal or
vertical, and no diagonal boundary is detected

102. STING: A Statistical Information Grid Approach (3)

WaveCluster (1998)
Sheikholeslami, Chatterjee, and Zhang (VLDB’98)
A multi-resolution clustering approach which applies
wavelet transform to the feature space
A wavelet transform is a signal processing
technique that decomposes a signal into different
frequency sub-band.
Both grid-based and density-based
Input parameters:
# of grid cells for each dimension
the wavelet, and the # of applications of wavelet
transform.
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103. WaveCluster (1998)

How to apply wavelet transform to find clusters
Summaries the data by imposing a multidimensional
grid structure onto data space
These multidimensional spatial data objects are
represented in a n-dimensional feature space
Apply wavelet transform on feature space to find the
dense regions in the feature space
Apply wavelet transform multiple times which result
in clusters at different scales from fine to coarse
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104. What is Wavelet (1)?

Wavelet Transform
Decomposes a signal into different frequency
subbands. (can be applied to n-dimensional
signals)
Data are transformed to preserve relative
distance between objects at different levels of
resolution.
Allows natural clusters to become more
distinguishable
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105. WaveCluster (1998)

What Is Wavelet (2)?
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106. Wavelet Transform

Quantization
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107. What Is Wavelet (2)?

Transformation
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108. Quantization

WaveCluster (1998)
Why is wavelet transformation useful for clustering
Unsupervised clustering
It uses hat-shape filters to emphasize region where
points cluster, but simultaneously to suppress weaker
information in their boundary
Effective removal of outliers
Multi-resolution
Cost efficiency
Major features:
Complexity O(N)
Detect arbitrary shaped clusters at different scales
Not sensitive to noise, not sensitive to input order
Only applicable to low dimensional data
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109. Transformation

CLIQUE (Clustering In QUEst)
Agrawal, Gehrke, Gunopulos, Raghavan (SIGMOD’98).
Automatically identifying subspaces of a high dimensional
data space that allow better clustering than original space
CLIQUE can be considered as both density-based and gridbased
It partitions each dimension into the same number of
equal length interval
It partitions an m-dimensional data space into nonoverlapping rectangular units
A unit is dense if the fraction of total data points
contained in the unit exceeds the input model parameter
A cluster is a maximal set of connected dense units
within a subspace
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110. WaveCluster (1998)

CLIQUE: The Major Steps
Partition the data space and find the number of points
that lie inside each cell of the partition.
Identify the subspaces that contain clusters using the
Apriori principle
Identify clusters:
Determine dense units in all subspaces of interests
Determine connected dense units in all subspaces of
interests.
Generate minimal description for the clusters
Determine maximal regions that cover a cluster of
connected dense units for each cluster
Determination of minimal cover for each cluster
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111. CLIQUE (Clustering In QUEst)

40
50
20
30
40
50
age
60
Vacation
=3
30
Vacation
(week)
0 1 2 3 4 5 6 7
Salary
(10,000)
0 1 2 3 4 5 6 7
20
age
60
30
50
age
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112. CLIQUE: The Major Steps

Strength and Weakness of CLIQUE
Strength
It automatically finds subspaces of the highest
dimensionality such that high density clusters exist in
those subspaces
It is insensitive to the order of records in input and
does not presume some canonical data distribution
It scales linearly with the size of input and has good
scalability as the number of dimensions in the data
increases
Weakness
The accuracy of the clustering result may be
degraded at the expense of simplicity of the method
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113.

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
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114. Strength and Weakness of CLIQUE

Model-Based Clustering Methods
Attempt to optimize the fit between the data and some
mathematical model
Statistical and AI approach
Conceptual clustering
A form of clustering in machine learning
Produces a classification scheme for a set of unlabeled objects
Finds characteristic description for each concept (class)
COBWEB (Fisher’87)
A popular a simple method of incremental conceptual learning
Creates a hierarchical clustering in the form of a classification
tree
Each node refers to a concept and contains a probabilistic
description of that concept
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115. Chapter 8. Cluster Analysis

COBWEB Clustering Method
A classification tree
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116. Model-Based Clustering Methods

More on Statistical-Based Clustering
Limitations of COBWEB
The assumption that the attributes are independent
of each other is often too strong because correlation
may exist
Not suitable for clustering large database data –
skewed tree and expensive probability distributions
CLASSIT
an extension of COBWEB for incremental clustering
of continuous data
suffers similar problems as COBWEB
AutoClass (Cheeseman and Stutz, 1996)
Uses Bayesian statistical analysis to estimate the
number of clusters
Popular in industry
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117. COBWEB Clustering Method

Other Model-Based Clustering
Methods
Neural network approaches
Represent each cluster as an exemplar, acting as a
“prototype” of the cluster
New objects are distributed to the cluster whose
exemplar is the most similar according to some
dostance measure
Competitive learning
Involves a hierarchical architecture of several units
(neurons)
Neurons compete in a “winner-takes-all” fashion for
the object currently being presented
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118. More on Statistical-Based Clustering

Model-Based Clustering Methods
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119. Other Model-Based Clustering Methods

Self-organizing feature maps (SOMs)
Clustering is also performed by having several
units competing for the current object
The unit whose weight vector is closest to the
current object wins
The winner and its neighbors learn by having
their weights adjusted
SOMs are believed to resemble processing that
can occur in the brain
Useful for visualizing high-dimensional data in
2- or 3-D space
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120. Model-Based Clustering Methods

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
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121. Self-organizing feature maps (SOMs)

What Is Outlier Discovery?
What are outliers?
The set of objects are considerably dissimilar from
the remainder of the data
Example: Sports: Michael Jordon, Wayne Gretzky,
...
Problem
Find top n outlier points
Applications:
Credit card fraud detection
Telecom fraud detection
Customer segmentation
Medical analysis
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122. Chapter 8. Cluster Analysis

Outlier Discovery:
Statistical Approaches
Assume a model underlying distribution that generates
data set (e.g. normal distribution)
Use discordancy tests depending on
data distribution
distribution parameter (e.g., mean, variance)
number of expected outliers
Drawbacks
most tests are for single attribute
In many cases, data distribution may not be known
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123. What Is Outlier Discovery?

Outlier Discovery: DistanceBased Approach
Introduced to counter the main limitations imposed by
statistical methods
We need multi-dimensional analysis without knowing
data distribution.
Distance-based outlier: A DB(p, D)-outlier is an object O
in a dataset T such that at least a fraction p of the
objects in T lies at a distance greater than D from O
Algorithms for mining distance-based outliers
Index-based algorithm
Nested-loop algorithm
Cell-based algorithm

124. Outlier Discovery: Statistical Approaches

Outlier Discovery: DeviationBased Approach
Identifies outliers by examining the main characteristics
of objects in a group
Objects that “deviate” from this description are
considered outliers
sequential exception technique
simulates the way in which humans can distinguish
unusual objects from among a series of supposedly
like objects
OLAP data cube technique
uses data cubes to identify regions of anomalies in
large multidimensional data
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125. Outlier Discovery: Distance-Based Approach

Chapter 8. Cluster Analysis
What is Cluster Analysis?
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods
Outlier Analysis
Summary
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126. Outlier Discovery: Deviation-Based Approach

Problems and Challenges
Considerable progress has been made in scalable
clustering methods
Partitioning: k-means, k-medoids, CLARANS
Hierarchical: BIRCH, CURE
Density-based: DBSCAN, CLIQUE, OPTICS
Grid-based: STING, WaveCluster
Model-based: Autoclass, Denclue, Cobweb
Current clustering techniques do not address all the
requirements adequately
Constraint-based clustering analysis: Constraints exist in
data space (bridges and highways) or in user queries
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127. Chapter 8. Cluster Analysis

Constraint-Based Clustering Analysis
Clustering analysis: less parameters but more user-desired
constraints, e.g., an ATM allocation problem
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128. Problems and Challenges

Clustering With Obstacle Objects
Not Taking obstacles into account
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Taking obstacles into account
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129. Constraint-Based Clustering Analysis

Summary
Cluster analysis groups objects based on their similarity
and has wide applications
Measure of similarity can be computed for various types
of data
Clustering algorithms can be categorized into partitioning
methods, hierarchical methods, density-based methods,
grid-based methods, and model-based methods
Outlier detection and analysis are very useful for fraud
detection, etc. and can be performed by statistical,
distance-based or deviation-based approaches
There are still lots of research issues on cluster analysis,
such as constraint-based clustering
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130. Clustering With Obstacle Objects

References (1)
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of
high dimensional data for data mining applications. SIGMOD'98
M. R. Anderberg. Cluster Analysis for Applications. Academic Press, 1973.
M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. Optics: Ordering points to identify
the clustering structure, SIGMOD’99.
P. Arabie, L. J. Hubert, and G. De Soete. Clustering and Classification. World Scietific, 1996
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering
clusters in large spatial databases. KDD'96.
M. Ester, H.-P. Kriegel, and X. Xu. Knowledge discovery in large spatial databases:
Focusing techniques for efficient class identification. SSD'95.
D. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning,
2:139-172, 1987.
D. Gibson, J. Kleinberg, and P. Raghavan. Clustering categorical data: An approach based
on dynamic systems. In Proc. VLDB’98.
S. Guha, R. Rastogi, and K. Shim. Cure: An efficient clustering algorithm for large
databases. SIGMOD'98.
A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Printice Hall, 1988.
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131. Summary

References (2)
L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster
Analysis. John Wiley & Sons, 1990.
E. Knorr and R. Ng. Algorithms for mining distance-based outliers in large datasets.
VLDB’98.
G. J. McLachlan and K.E. Bkasford. Mixture Models: Inference and Applications to
Clustering. John Wiley and Sons, 1988.
P. Michaud. Clustering techniques. Future Generation Computer systems, 13, 1997.
R. Ng and J. Han. Efficient and effective clustering method for spatial data mining.
VLDB'94.
E. Schikuta. Grid clustering: An efficient hierarchical clustering method for very large
data sets. Proc. 1996 Int. Conf. on Pattern Recognition, 101-105.
G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution
clustering approach for very large spatial databases. VLDB’98.
W. Wang, Yang, R. Muntz, STING: A Statistical Information grid Approach to Spatial
Data Mining, VLDB’97.
T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH : an efficient data clustering method
for very large databases. SIGMOD'96.
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